Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...Simplilearn
This presentation about Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We'll also implement a neural network manually. Finally, we'll code a neural network in Python using TensorFlow.
Below topics are explained in this Deep Learning with Python presentation:
1. What is Deep Learning
2. Biological versus Artificial Intelligence
3. What is a Neural Network
4. Activation function
5. Cost function
6. How do Neural Networks work
7. How do Neural Networks learn
8. Implementing the Neural Network
9. Gradient descent
10. Deep Learning platforms
11. Introduction to TensoFlow
12. Implementation in TensorFlow
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
An Incomplete Introduction to Artificial IntelligenceSteven Beeckman
This is the releasable version of an internal presentation on artificial intelligence. It includes a brief history of AI, a mathematical approach to deep learning and an overview of some use-cases of deep learning.
Spellcheck: "General Adversarial Networks" are actually called "Generative Adversarial Networks".
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...Simplilearn
This presentation about Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We'll also implement a neural network manually. Finally, we'll code a neural network in Python using TensorFlow.
Below topics are explained in this Deep Learning with Python presentation:
1. What is Deep Learning
2. Biological versus Artificial Intelligence
3. What is a Neural Network
4. Activation function
5. Cost function
6. How do Neural Networks work
7. How do Neural Networks learn
8. Implementing the Neural Network
9. Gradient descent
10. Deep Learning platforms
11. Introduction to TensoFlow
12. Implementation in TensorFlow
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
An Incomplete Introduction to Artificial IntelligenceSteven Beeckman
This is the releasable version of an internal presentation on artificial intelligence. It includes a brief history of AI, a mathematical approach to deep learning and an overview of some use-cases of deep learning.
Spellcheck: "General Adversarial Networks" are actually called "Generative Adversarial Networks".
Building a cutting-edge data processing environment on a budgetGael Varoquaux
As a penniless academic I wanted to do "big data" for science. Open source, Python, and simple patterns were the way forward. Staying on top of todays growing datasets is an arm race. Data analytics machinery —clusters, NOSQL, visualization, Hadoop, machine learning, ...— can spread a team's resources thin. Focusing on simple patterns, lightweight technologies, and a good understanding of the applications gets us most of the way for a fraction of the cost.
I will present a personal perspective on ten years of scientific data processing with Python. What are the emerging patterns in data processing? How can modern data-mining ideas be used without a big engineering team? What constraints and design trade-offs govern software projects like scikit-learn, Mayavi, or joblib? How can we make the most out of distributed hardware with simple framework-less code?
Adam Streck - Reinforcement Learning in Unity. Teach Your Monsters - Codemoti...Codemotion
With the advent of deep learning many of the tasks in computer science that have been deemed impossible suddenly became only a few clicks away. One of the approaches made available is reinforcement learning - a method for solving problems by establishing an action-reward scheme. Combined with the power and availability of the general-purpose game engines, anyone with a rudimentary knowledge of the topic can create and train their virtual creatures. In this talk we will use this power to solve one of the most frustratingly difficult (according to the internet) games of our era.
Adam Streck - Reinforcement Learning in Unity - Teach Your Monsters - Codemot...Codemotion
With the advent of deep learning many of the tasks in computer science that have been deemed impossible suddenly became only a few clicks away. One of the approaches made available is reinforcement learning - a method for solving problems by establishing an action-reward scheme. Combined with the power and availability of the general-purpose game engines, anyone with a rudimentary knowledge of the topic can create and train their virtual creatures. In this talk we will use this power to solve one of the most frustratingly difficult (according to the internet) games of our era.
Using Topological Data Analysis on your BigDataAnalyticsWeek
Synopsis:
Topological Data Analysis (TDA) is a framework for data analysis and machine learning and represents a breakthrough in how to effectively use geometric and topological information to solve 'Big Data' problems. TDA provides meaningful summaries (in a technical sense to be described) and insights into complex data problems. In this talk, Anthony will begin with an overview of TDA and describe the core algorithm that is utilized. This talk will include both the theory and real world problems that have been solved using TDA. After this talk, attendees will understand how the underlying TDA algorithm works and how it improves on existing “classical” data analysis techniques as well as how it provides a framework for many machine learning algorithms and tasks.
Speaker:
Anthony Bak, Senior Data Scientist, Ayasdi
Prior to coming to Ayasdi, Anthony was at Stanford University where he did a postdoc with Ayasdi co-founder Gunnar Carlsson, working on new methods and applications of Topological Data Analysis. He completed his Ph.D. work in algebraic geometry with applications to string theory at the University of Pennsylvania and ,along the way, he worked at the Max Planck Institute in Germany, Mount Holyoke College in Germany, and the American Institute of Mathematics in California.
The Music Information Retrieval Evaluation eXchange (MIREX) is a valuable community service, having established standard datasets, metrics, baselines, methodologies, and infrastructure for comparing MIR methods. While MIREX has managed to successfully maintain operations for over a decade, its long-term sustainability is at risk without considerable ongoing financial support. The imposed constraint that input data cannot be made freely available to participants necessitates that all algorithms run on centralized computational resources, which are administered by a limited number of people. This incurs an approximately linear cost with the number of submissions, exacting significant tolls on both human and financial resources, such that the current paradigm becomes less tenable as participation increases. To alleviate the recurring costs of future evaluation campaigns, we propose a distributed, community-centric paradigm for system evaluation, built upon the principles of openness, transparency, reproducibility, and incremental evaluation. We argue that this proposal has the potential to reduce operating costs to sustainable levels. Moreover, the proposed paradigm would improve scalability, and eventually result in the release of large, open datasets for improving both MIR techniques and evaluation methods.
Vowpal Platypus: Very Fast Multi-Core Machine Learning in Python.Peter Hurford
Vowpal Platypus is a general use, lightweight Python wrapper built on Vowpal Wabbit, that uses online learning to achieve great results. https://github.com/peterhurford/vowpal_platypus
Demystifying Machine Learning - How to give your business superpowers.10x Nation
A "no math" introduction to machine learning concepts. Touches on various ML architectures, including neural networks and deep learning. Includes tons of resource links.
Crafting Recommenders: the Shallow and the Deep of it! Sudeep Das, Ph.D.
I present a brief review, and an outlook on the rapid changes happening in the field of recommendation engine research on the heels of the deep learning revolution!
Similar to Convolutional neural network in practice (20)
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
4. Glossary of AI terms
From Roger Parloff, WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE (Fortune, 2016).
5. Definitions
What is AI ?
“Artificial intelligence is that activity devoted to making machines
intelligent, and intelligence is that quality that enables an entity to
function appropriately and with foresight in its environment.”
Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements (Cambridge, UK: Cambridge University Press, 2010).
“a computerized system that exhibits behavior that is commonly thought
of as requiring intelligence”
Executive Office of the President National Science and Technology Council Committee on Technology: PREPARING FOR THE FUTURE OF
ARTIFICIAL INTELLIGENCE (2016).
“any technique that enables computers to mimic human intelligence”
Roger Parloff, WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE (Fortune, 2016).
6. My diagram of AI terms
Environment
Data, Rules,
Feedbacks ...
Teaching
Self-Learning,
Engineering
...
AI
y = f(x)
Catf F18f
14. 5 Tribes of AI researchers
Symbolists
(Rule, Logic-based)
Connectionists
(PDP assumption)
Bayesians EvolutionistsAnalogizers
vs.
15. Deep learning has had a long
and rich history !
● 3 re-brandings.
○ Cybernetics ( 1940s ~ 1960s )
○ Artificial Neural Networks ( 1980s ~ 1990s)
○ Deep learning ( 2006 ~ )
16. Nothing new !
● Alexnet 2012
○ based on CNN ( LeCunn, 1989 )
● Alpha Go
○ based on Reinforcement learning and
MCTS ( Sutton, 1998 )
17. So, why now ?
● Computing Power
● Large labelled dataset
● Algorithm
18. Size of neural networks
From Ian Goodfellow, Deep Learning (MIT press, 2016).
Singularity or Transcendence ?
20. Brief history of deep learning
From Roger Parloff, WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE (Fortune, 2016).
1st Boom 2nd Boom1st Winter
21. Brief history of deep learning
From Roger Parloff, WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE (Fortune, 2016).
22. Brief history of deep learning
From Roger Parloff, WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE (Fortune, 2016).
2nd Winter
23. Brief history of deep learning
From Roger Parloff, WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE (Fortune, 2016).
3rd Boom
24. Brief history of deep learning
From Roger Parloff, WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE (Fortune, 2016).
25. So, when 3rd winter ?
Nope !!!
● Features are mandatory in every AI
problem.
● Deep learning is cheap learning!
(Though someone can disprove the PDP assumptions,
deep learning is the best practical tool in
representation learning.)
26. Biz trends after Oct.2012.
● 4 big players leading this sector.
● Bloody hiring war.
○ Along the lines of NFL football players.
27. Biz trend after Oct.2012.
● 2 leading research firms.
● 60+ startups
38. So what can we do with AI?
● Simply, it’s sophisticated software
writing software.
True personalization at scale!!!
39. Is AI really necessary ?
“a lot of S&P 500 CEOs wished they had started
thinking sooner than they did about their Internet
strategy. I think five years from now there will be
a number of S&P 500 CEOs that will wish
they’d started thinking earlier about their AI
strategy.”
“AI is the new electricity, just as 100 years ago
electricity transformed industry after industry, AI
will now do the same.”
Andrew Ng., chief scientist at Baidu Research.
53. Parameters of convolution
● Kernel size
○ ( row, col, in_channel, out_channel)
● Padding
○ SAME, VALID, FULL
● Stride
○ if S > 1, use even kernel size F >
S * 2
54. 1 dimensional convolution
pad(P=1) pad(P=1) pad(P=1)
stride(S=1)
kernel
(F=3)
stride(S=2)
● ‘SAME’(or ‘HALF’) pad size = (F - 1) * S / 2
● ‘VALID’ pad size = 0
● ‘FULL’ pad size : not used nowadays
61. Pooling vs. Striding
● Same in the downsample aspect
● But, different in the location aspect
○ Location is lost in Pooling
○ Location is preserved in Striding
● Nowadays, striding is more popular
○ some kind of learnable pooling
62. Kernel initialization
● Random number between -1 and 1
○ Orthogonality ( I.I.D. )
○ Uniform or Gaussian random
● Scale is paramount.
○ Adjust such that out(activation)
values have mean 0 and variance 1
○ If you encounter NaN, that may be
because of ill scale.
65. Initialization guide
● Xavier(or Glorot) initialization
○ http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a
.pdf
● He initialization
○ Good for RELU nonlinearity
○ https://arxiv.org/abs/1502.01852
● Use batch normalization if possible
○ Immune to ill-scaled initialization
67. Guide
● Start from robust baseline
○ 3 choices
■ VGG, Inception-v3, Resnet
● Smaller and deeper
● Towards getting rid of POOL and
final dense layer
● BN and skip connection are popular
82. Summary
● Start from Resnet-50
● Use He’s initialization
● learning rate : 0.001 (with BN), 0.0001
(without BN)
● Use Adam ( should be alpha < beta ) optim
○ alpha=0.9, beta=0.999 (with easy training)
○ alpha=0.5, beta=0.95 (with hard training)
83. Summary
● Minimize hyper-parameter tuning or
architecture modification.
○ Deep learning is highly nonlinear and
count-intuitive
○ Grid or random search is expensive
94. Augmentation
● 3 types of augmentation
○ Traing data augmentation
○ Evaluation augmentation
○ Label augmentation
● Augmentation is mandatory
○ If you have really big data, then augment
data and increase model capacity
95. Training Augmentation
● Random crop/scale
○ random L in range [256, 480]
○ Resize training image, short side = L
○ Sample random 224x224 patch
99. Testing Augmentation
● Multi-scale testing
○ Fully convolutional layer is mandatory
○ Random L in range [224, 640]
○ Resize training image such that short side
= L
○ Average(or max) scores
● Used in Resnet
108. Simple recipe
CE loss
L2(MSE) loss
Joint-learning ( Multi-task learning )
or
Separate learning
From : http://cs231n.stanford.edu/slides/winter1516_lecture8.pdf
131. ESPCN ( Efficient Sub-pixel
CNN)
Periodic
shuffle
Wenzhe, Real-Time Single Image and Video Super-Resolution Using and Efficient Sub-Pixel Convolutional
Neural Network, 2016
132. L2 loss issue
Christian, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, 2016
139. Summary
● Model temporal motion locally ( 3D CONV )
● Model temporal motion globally ( RNN )
● Hybrids of both
● IMHO, RNN will be replaced with 1D
convolution dilated (atrous convolution)
150. Results
( From Ian. J. Fellow et al. Generative Adverserial Networks. 2014. )
( From P. Kingma et al. Auto-Encoding Variational Bayes. 2013. )
151. Pitfalls of GAN
● Very difficult to train.
○ No guarantee to Nash Equilibrium.
■ Tim Salimans et al, Improved Techniques for Training GANS, 2016.
■ Junbo Zhao et al, Energy-based Generative Adversarial Network,
2016.
● Cannot control generated data.
○ How can we condition generating
function G(x)?
152. InfoGAN
Xi Chen et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative
Adversarial Nets, 2016 ( https://arxiv.org/abs/1606.03657 )
● Add mutual Information regularizer for inducing latent
codes to original GAN.
159. Features of GAN
● Unsupervised
○ No labelled data used
● End-to-end
○ No human feature engineering
○ No prior nor assumption
● High fidelity
○ automatic highly non-linear pattern finding
⇒ Currently, SOTA in image generation.
